A Comparative Performance Of Gray Level Image Thresholding Using Normalized Graph Cut Based Standard S Membership Function

In this research paper, we use a normalized graph cut measure as a thresholding principle to separate an object from the background based on the standard S membership function. The implementation of the proposed algorithm known as fuzzy normalized graph cut method. This proposed algorithm compared with the fuzzy entropy method [25], Kittler [11], Rosin [21], Sauvola [23] and Wolf [33] method. Moreover, we examine that in most cases, our algorithm gives the lowest absolute error that improves the segmentation process of gray images. Finally, we change different parameter values in fuzzy normalized graph cut and the effect of the substitutes is studied. Also, we analyze the computational complexity of fuzzy weight matrix (fuzzification) results with a weight matrix (classical) results.